
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import seaborn as sns
import plotly.express as px
import plotly.graph_objects as go
import geopandas as gpd
%matplotlib inline


# dünya onaylanmış vaka sayıları
confirmed = pd.read_csv('confirmed.csv')
# dünya ölüm sayıları
deaths = pd.read_csv('deaths.csv')
# dünya iyileşen sayıları
recovered = pd.read_csv('recovered.csv')


confirmed.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 276 entries, 0 to 275
Columns: 503 entries, Province/State to 6/3/21
dtypes: float64(2), int64(499), object(2)
memory usage: 1.1+ MB


deaths.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 276 entries, 0 to 275
Columns: 503 entries, Province/State to 6/3/21
dtypes: float64(2), int64(499), object(2)
memory usage: 1.1+ MB


recovered.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 261 entries, 0 to 260
Columns: 503 entries, Province/State to 6/3/21
dtypes: float64(2), int64(499), object(2)
memory usage: 1.0+ MB


confirmed.describe().T


deaths.describe().T


recovered.describe().T


confirmed


deaths


recovered


confirmed = confirmed.drop(columns=["Province/State","Lat","Long"])
deaths = deaths.drop(columns=["Province/State","Lat","Long"])
recovered = recovered.drop(columns=["Province/State","Lat","Long"])


confirmed


confirmed=confirmed.groupby("Country/Region").aggregate(np.sum).T
deaths=deaths.groupby("Country/Region").aggregate(np.sum).T
recovered=recovered.groupby("Country/Region").aggregate(np.sum).T


confirmed


confirmed.index.name="Date"
deaths.index.name="Date"
recovered.index.name="Date"


confirmed


confirmed=confirmed.reset_index()
deaths=deaths.reset_index()
recovered=recovered.reset_index()


confirmed


confirmed=confirmed.melt(id_vars=["Date"],var_name="Country",value_name="Confirmed")
deaths=deaths.melt(id_vars=["Date"],var_name="Country",value_name="Deaths")
recovered=recovered.melt(id_vars=["Date"],var_name="Country",value_name="Recovered")


confirmed


deaths


recovered


confirmed["Date"]=pd.to_datetime(confirmed["Date"])
deaths["Date"]=pd.to_datetime(deaths["Date"])
recovered["Date"]=pd.to_datetime(recovered["Date"])


confirmed["Date"]=confirmed["Date"].dt.strftime("%Y/%m/%d")
deaths["Date"]=deaths["Date"].dt.strftime("%Y/%m/%d")
recovered["Date"]=recovered["Date"].dt.strftime("%Y/%m/%d")


confirmed


deaths


recovered


max_date=confirmed["Date"].max()
print(type(max_date))
max_date

<class 'str'>

'2021/06/03'


max_confirmed=confirmed[confirmed["Date"]==max_date]
max_deaths=deaths[deaths["Date"]==max_date]
max_recovered=recovered[recovered["Date"]==max_date]
max_confirmed


total_confirmed=max_confirmed["Confirmed"].sum()
total_confirmed

172169929


total_deaths=deaths["Deaths"].sum()
total_deaths

642156210


total_recovered=recovered["Recovered"].sum()
total_recovered

15989001302


fig=go.Figure()

fig.add_trace(go.Indicator(mode="number",value=int(total_confirmed),number={"valueformat":"0.f"},
             title={"text":"Toplam Vaka"},domain={"row":0,"column":0}))

fig.add_trace(go.Indicator(mode="number",value=int(total_deaths),number={"valueformat":"0.f"},
             title={"text":"Toplam Ölüm"},domain={"row":0,"column":1}))


fig.add_trace(go.Indicator(mode="number",value=int(total_recovered),number={"valueformat":"0.f"},
             title={"text":"Toplam İyileşen"},domain={"row":1,"column":0}))


fig.update_layout(grid={"rows":2,"columns":2,"pattern":"independent"})
fig.show()


fig=px.bar(max_confirmed,x="Country",y="Confirmed", title="Ülkelere Göre Vaka Sayıları")
fig.update_layout(
    title={
        'text': "Ülkelere Göre Vaka Sayıları",
        'y':0.9,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'})

fig.show()


fig=px.choropleth(max_confirmed,locations="Country",locationmode="country names",
                  color_continuous_scale="dense",
                  color=np.log10(max_confirmed["Confirmed"]),range_color=(0,9))
fig.show()


import plotly.offline as py
py.init_notebook_mode(connected=True)

countries = np.unique(confirmed['Country'])
mean_conf = []
for country in countries:
    mean_conf.append(confirmed[confirmed['Country'] == country]['Confirmed'].sum())

    data = [ dict(
        type = 'choropleth',
        locations = countries,
        z = mean_conf,
        locationmode = 'country names',
        text = countries,
        marker = dict(
            line = dict(color = 'rgb(0,0,0)', width = 1)),
            colorbar = dict(autotick = True, tickprefix = '', 
            title = 'Count')
            )
       ]

    layout = dict(
    title = 'Vaka Sayıları',
    geo = dict(
        showframe = False,
        showocean = True,
        oceancolor = 'rgb(0,255,255)',
        projection = dict(
        type = 'orthographic',
            rotation = dict(
                    lon = 60,
                    lat = 10),
        ),
        lonaxis =  dict(
                showgrid = True,
                gridcolor = 'rgb(102, 102, 102)'
            ),
        lataxis = dict(
                showgrid = True,
                gridcolor = 'rgb(102, 102, 102)'
                )
            ),
        )

fig = dict(data=data, layout=layout)
py.iplot(fig, validate=False, filename='worldmap')


fig=px.scatter(confirmed,x="Date",y="Confirmed",color="Country")
fig.update_traces(opacity=0.6)
fig.update_layout(
    title={
        'text': "Zamana Göre Vaka Sayıları",
        'y':0.95,
        'x':0.55,
        'xanchor': 'center',
        'yanchor': 'top'})
fig.show()


fig=px.bar(max_confirmed.sort_values("Confirmed",ascending=False).head(10),x="Country",y="Confirmed")
fig.update_layout(
    title={
        'text': "Vaka Sayısı En Yüksek 10 Ülke",
        'y':0.95,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'})
fig.show()


fig = go.Figure()
fig.add_trace(go.Scatter(
                x=confirmed.Country,
                y=confirmed['Confirmed'],
                name="Confirmed",
                line_color='deepskyblue',
                opacity=0.8))

fig.add_trace(go.Scatter(
                x=recovered.Country,
                y=recovered['Recovered'],
                name="Recovered",
                line_color='dimgray',
                opacity=0.8))
fig.update_layout(    title={
        'text': "Ülkelerin Vaka/İyileşen Grafiği",
        'y':0.95,
        'x':0.5,
        'xanchor': 'center',
        'yanchor': 'top'},
        xaxis_rangeslider_visible=True)
fig.show()


figure=px.line(confirmed[confirmed["Country"]=="Turkey"],x="Date",y="Confirmed")
figure


series_confirmed = pd.Series.to_list(max_confirmed['Confirmed'])
series_deaths = pd.Series.to_list(max_deaths['Deaths'])
series_recovered = pd.Series.to_list(max_recovered['Recovered'])
np_confirmed = np.array(series_confirmed)
np_deaths = np.array(series_deaths)
np_recovered = np.array(series_recovered)

	count	mean	std	min	25%	50%	75%	max
Lat	274.0	20.447559	2.518984e+01	-51.7963	4.933349	21.607878	40.950592	7.170690e+01
Long	274.0	22.328281	7.436910e+01	-178.1165	-22.036550	20.921188	83.380449	1.780650e+02
1/22/20	276.0	2.018116	2.678174e+01	0.0000	0.000000	0.000000	0.000000	4.440000e+02
1/23/20	276.0	2.373188	2.687957e+01	0.0000	0.000000	0.000000	0.000000	4.440000e+02
1/24/20	276.0	3.409420	3.346416e+01	0.0000	0.000000	0.000000	0.000000	5.490000e+02
...	...	...	...	...	...	...	...	...
5/30/21	276.0	617194.481884	2.890908e+06	0.0000	1352.250000	19177.000000	254386.250000	3.326173e+07
5/31/21	276.0	618565.985507	2.896575e+06	0.0000	1352.250000	19097.500000	254530.250000	3.326751e+07
6/1/21	276.0	620244.927536	2.904189e+06	0.0000	1352.250000	19100.000000	254962.750000	3.329045e+07
6/2/21	276.0	622031.927536	2.911920e+06	0.0000	1356.500000	19102.000000	255755.500000	3.330736e+07
6/3/21	276.0	623804.090580	2.919518e+06	0.0000	1358.750000	19108.500000	256173.750000	3.332644e+07

	count	mean	std	min	25%	50%	75%	max
Lat	274.0	20.447559	25.189838	-51.7963	4.933349	21.607878	40.950592	71.7069
Long	274.0	22.328281	74.369096	-178.1165	-22.036550	20.921188	83.380449	178.0650
1/22/20	276.0	0.061594	1.023280	0.0000	0.000000	0.000000	0.000000	17.0000
1/23/20	276.0	0.065217	1.024830	0.0000	0.000000	0.000000	0.000000	17.0000
1/24/20	276.0	0.094203	1.446690	0.0000	0.000000	0.000000	0.000000	24.0000
...	...	...	...	...	...	...	...	...
5/30/21	276.0	12830.931159	54032.103939	0.0000	8.750000	268.000000	3528.250000	594443.0000
5/31/21	276.0	12862.380435	54137.898125	0.0000	8.750000	270.000000	3529.750000	594585.0000
6/1/21	276.0	12917.083333	54378.469075	0.0000	8.750000	275.000000	3535.750000	595223.0000
6/2/21	276.0	13375.594203	55428.221463	0.0000	8.750000	276.000000	3539.500000	595833.0000
6/3/21	276.0	13411.496377	55574.306719	0.0000	8.750000	283.500000	3542.250000	596434.0000

	count	mean	std	min	25%	50%	75%	max
Lat	260.0	19.016304	2.463273e+01	-51.7963	4.562000	19.584785	38.991325	7.170690e+01
Long	260.0	27.737245	7.184781e+01	-178.1165	-9.496274	24.242250	90.375625	1.780650e+02
1/22/20	261.0	0.114943	1.737097e+00	0.0000	0.000000	0.000000	0.000000	2.800000e+01
1/23/20	261.0	0.122605	1.740995e+00	0.0000	0.000000	0.000000	0.000000	2.800000e+01
1/24/20	261.0	0.149425	1.932687e+00	0.0000	0.000000	0.000000	0.000000	3.100000e+01
...	...	...	...	...	...	...	...	...
5/30/21	261.0	412377.770115	1.932424e+06	0.0000	1039.000000	13248.000000	191475.000000	2.569234e+07
5/31/21	261.0	414405.237548	1.947267e+06	0.0000	1039.000000	13255.000000	192112.000000	2.594763e+07
6/1/21	261.0	416642.337165	1.962782e+06	0.0000	1039.000000	13256.000000	192823.000000	2.617908e+07
6/2/21	261.0	418666.233716	1.976202e+06	0.0000	1040.000000	13256.000000	193491.000000	2.639058e+07
6/3/21	261.0	420309.950192	1.987374e+06	0.0000	1041.000000	13260.000000	194291.000000	2.659766e+07

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	5/25/21	5/26/21	5/27/21	5/28/21	5/29/21	5/30/21	5/31/21	6/1/21	6/2/21	6/3/21
0	NaN	Afghanistan	33.939110	67.709953	0	0	0	0	0	0	...	66903	67743	68366	69130	70111	70761	71838	72977	74026	75119
1	NaN	Albania	41.153300	20.168300	0	0	0	0	0	0	...	132229	132244	132264	132285	132297	132309	132315	132337	132351	132360
2	NaN	Algeria	28.033900	1.659600	0	0	0	0	0	0	...	127361	127646	127926	128198	128456	128725	128913	129218	129640	129976
3	NaN	Andorra	42.506300	1.521800	0	0	0	0	0	0	...	13664	13671	13682	13693	13693	13693	13727	13729	13744	13752
4	NaN	Angola	-11.202700	17.873900	0	0	0	0	0	0	...	32933	33338	33607	33944	34180	34366	34551	34752	34960	35140
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
271	NaN	Vietnam	14.058324	108.277199	0	2	2	2	2	2	...	5931	6086	6356	6396	6908	7107	7432	7625	7870	8063
272	NaN	West Bank and Gaza	31.952200	35.233200	0	0	0	0	0	0	...	306334	306795	306795	307569	307838	308048	308350	308732	309036	309333
273	NaN	Yemen	15.552727	48.516388	0	0	0	0	0	0	...	6670	6688	6696	6723	6731	6737	6742	6751	6759	6767
274	NaN	Zambia	-13.133897	27.849332	0	0	0	0	0	0	...	93428	93627	93947	94430	94751	95050	95263	95821	96563	97388
275	NaN	Zimbabwe	-19.015438	29.154857	0	0	0	0	0	0	...	38706	38819	38854	38918	38933	38944	38961	38998	39031	39092

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	5/25/21	5/26/21	5/27/21	5/28/21	5/29/21	5/30/21	5/31/21	6/1/21	6/2/21	6/3/21
0	NaN	Afghanistan	33.939110	67.709953	0	0	0	0	0	0	...	2836	2855	2869	2881	2899	2919	2944	2973	3007	3034
1	NaN	Albania	41.153300	20.168300	0	0	0	0	0	0	...	2447	2447	2447	2448	2449	2450	2451	2451	2451	2451
2	NaN	Algeria	28.033900	1.659600	0	0	0	0	0	0	...	3433	3440	3448	3455	3460	3465	3472	3480	3490	3497
3	NaN	Andorra	42.506300	1.521800	0	0	0	0	0	0	...	127	127	127	127	127	127	127	127	127	127
4	NaN	Angola	-11.202700	17.873900	0	0	0	0	0	0	...	735	742	745	749	757	764	766	772	780	784
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
271	NaN	Vietnam	14.058324	108.277199	0	0	0	0	0	0	...	44	45	46	47	47	47	47	48	49	49
272	NaN	West Bank and Gaza	31.952200	35.233200	0	0	0	0	0	0	...	3480	3483	3483	3489	3492	3495	3497	3503	3507	3509
273	NaN	Yemen	15.552727	48.516388	0	0	0	0	0	0	...	1311	1313	1315	1316	1319	1320	1321	1322	1323	1325
274	NaN	Zambia	-13.133897	27.849332	0	0	0	0	0	0	...	1271	1273	1275	1275	1276	1278	1281	1282	1284	1288
275	NaN	Zimbabwe	-19.015438	29.154857	0	0	0	0	0	0	...	1587	1589	1592	1592	1594	1594	1594	1599	1599	1604

CBS UYGULAMASI

Covid19 Veri Analizi ve Görselleştirme

Modüllerin Yüklenmesi¶

Veri Setlerinin Yüklenmesi¶

Veri Seti Hakkında Bilgi¶

İstatistiksel Veriler¶

Veri Seti¶

Gereksiz Sütunların Silinmesi¶

Ülke İsimlerine Göre Gruplama Toplamını ve Transpozunu Alma¶

İndeks İsmini Değiştirme¶

İndeks Resetleme¶

Tarihe Göre Verileri Bir Arada Gösterme¶

Date İndeksini Tarih Formatına Çevirme¶

pd.to_datetime fonksiyonu ile dönüştürme işlemi ¶

Tarih Formatı ¶

Toplam Vaka Sayısı¶

Toplam Ölüm Sayısı¶

Toplam İyileşen Sayısı¶

Ülkelere Göre Vaka Sayıları¶

Vaka Sayıları¶

Vaka Sayıları¶

Zamana Göre Vaka Sayıları¶

Vaka Sayısı En Yüksek 10 Ülke¶

Ülkelerin Vaka/İyileşen Grafiği¶

Türkiye Zamana Göre Vaka Sayısı Artışı¶

Çoklu Grafik¶

Çoklu grafik için kolonları diziye çevirme¶

Country/Region	Afghanistan	Albania	Algeria	Andorra	Angola	Antigua and Barbuda	Argentina	Armenia	Australia	Austria	...	United Kingdom	Uruguay	Uzbekistan	Vanuatu	Venezuela	Vietnam	West Bank and Gaza	Yemen	Zambia	Zimbabwe
Date
1/22/20	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1/23/20	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	2	0	0	0	0
1/24/20	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	2	0	0	0	0
1/25/20	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	2	0	0	0	0
1/26/20	0	0	0	0	0	0	0	0	4	0	...	0	0	0	0	0	2	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
5/30/21	70761	132309	128725	13693	34366	1259	3753609	222636	30105	644586	...	4499937	291488	100124	4	232800	7107	308048	6737	95050	38944
5/31/21	71838	132315	128913	13727	34551	1260	3781784	222670	30118	644815	...	4503231	294503	100335	4	234165	7432	308350	6742	95263	38961
6/1/21	72977	132337	129218	13729	34752	1260	3817139	222778	30124	645152	...	4506333	298006	100495	4	235567	7625	308732	6751	95821	38998
6/2/21	74026	132351	129640	13744	34960	1262	3852156	222870	30137	645552	...	4510597	301524	100726	4	236755	7870	309036	6759	96563	39031
6/3/21	75119	132360	129976	13752	35140	1262	3884447	222978	30141	645834	...	4515778	304411	100997	4	238013	8063	309333	6767	97388	39092

	Date	Country	Confirmed
0	2020/01/22	Afghanistan	0
1	2020/01/23	Afghanistan	0
2	2020/01/24	Afghanistan	0
3	2020/01/25	Afghanistan	0
4	2020/01/26	Afghanistan	0
...	...	...	...
96302	2021/05/30	Zimbabwe	38944
96303	2021/05/31	Zimbabwe	38961
96304	2021/06/01	Zimbabwe	38998
96305	2021/06/02	Zimbabwe	39031
96306	2021/06/03	Zimbabwe	39092

	Province/State	Country/Region	Lat	Long	1/22/20	1/23/20	1/24/20	1/25/20	1/26/20	1/27/20	...	5/25/21	5/26/21	5/27/21	5/28/21	5/29/21	5/30/21	5/31/21	6/1/21	6/2/21	6/3/21
0	NaN	Afghanistan	33.939110	67.709953	0	0	0	0	0	0	...	56518	56711	56962	57119	57281	57450	57629	57741	57963	58070
1	NaN	Albania	41.153300	20.168300	0	0	0	0	0	0	...	128907	128978	129042	129097	129215	129308	129431	129473	129521	129566
2	NaN	Algeria	28.033900	1.659600	0	0	0	0	0	0	...	88672	88861	89040	89232	89419	89625	89839	90057	90281	90517
3	NaN	Andorra	42.506300	1.521800	0	0	0	0	0	0	...	13263	13381	13405	13416	13416	13416	13458	13479	13507	13527
4	NaN	Angola	-11.202700	17.873900	0	0	0	0	0	0	...	27204	27467	27529	27577	27646	27766	28079	28190	28264	28646
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
256	NaN	Vietnam	14.058324	108.277199	0	0	0	0	0	0	...	2794	2853	2853	2896	2896	2950	3029	3043	3085	3085
257	NaN	West Bank and Gaza	31.952200	35.233200	0	0	0	0	0	0	...	299024	299559	299559	300125	300524	300661	300776	300919	301213	301443
258	NaN	Yemen	15.552727	48.516388	0	0	0	0	0	0	...	3273	3306	3339	3375	3399	3427	3445	3472	3472	3484
259	NaN	Zambia	-13.133897	27.849332	0	0	0	0	0	0	...	91221	91239	91321	91443	91594	91752	91956	92039	92108	92320
260	NaN	Zimbabwe	-19.015438	29.154857	0	0	0	0	0	0	...	36517	36531	36541	36563	36578	36591	36594	36624	36661	36680